SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2931–2940 of 15113 papers

TitleStatusHype
A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback—0
Prioritized Soft Q-Decomposition for Lexicographic Reinforcement LearningCode0
AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model—0
Towards a Unified Framework for Sequential Decision Making—0
Learning and reusing primitive behaviours to improve Hindsight Experience Replay sample efficiencyCode0
Blending Imitation and Reinforcement Learning for Robust Policy Improvement—0
REMEDI: REinforcement learning-driven adaptive MEtabolism modeling of primary sclerosing cholangitis DIsease progression—0
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning—0
Improving Dialogue Management: Quality Datasets vs ModelsCode0
From Bandits Model to Deep Deterministic Policy Gradient, Reinforcement Learning with Contextual Information—0
Show:102550
← PrevPage 294 of 1512Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified